Energy Efficiency Services Research Articles

Electricity user behavior analysis and marketing strategy based on internet of things and big data

This paper examines power user behavior and the design of marketing strategies, using a case study of Smart Community A. We explore how advanced analytical models are used to enhance energy efficiency and user services. First, we apply spectral clustering to refine user segmentation and identify distinct electricity consumption patterns among different groups. Then, the Hidden Markov Model (HMM) analyzes user behavior, uncovering shifts in consumption habits and enabling personalized service offerings. Next, the ARIMA model predicts electricity consumption trends, guiding grid scheduling and resource allocation. Based on these analyses, we develop targeted marketing strategies, such as dynamic pricing and energy-saving incentives, which boost user engagement and reduce energy usage. Through an IoT and big data-driven interactive marketing platform, we enhance user experience and foster a culture of energy conservation. Finally, a feedback mechanism ensures continuous improvement and maximizes the effectiveness of the marketing strategies.

Harnessing machine learning algorithms to unveil energy efficiency investment archetypes

Increasing transparency about the performance of different projects is crucial to reducing the heterogeneity in the energy efficiency services market, thereby upscaling investments. In this context, machine learning algorithms could assist in identifying and analyzing energy efficiency project archetypes, although this field has so far been explored with a limited view in the literature. This paper aims to address this gap by identifying energy efficiency investment families and the determinant factors of the classification scheme, using machine learning. In this effort, it hinges on a wide range of indicators from implemented projects around Europe and the USA, including investment profitability, initial investment, risk of failure, intervention type, life measure, region of implementation and building type. The analysis employs two clustering approaches, namely Partitioning Around Medoids (PAM) and K-means, determining the number of clusters based on the Silhouette index and total within–cluster sum of squares. The results indicate that energy efficiency investments can be classified into three categories: (i) “junk investments”, characterized by low–profitability (IRR∼10%), moderate risk, and extended horizons; (ii) “safe profitability”, distinguished by high profitability (IRR∼30%) and minimal risk; and (iii) “high stakes”, described by exceptionally high profitability (IRR∼40%), coupled with a substantial risk. Next to profitability and risk of failure, also energy efficiency intervention and building type (sector) emerge among the most influential factors in the classification scheme. Feature importance shows a significant sensitivity to the chosen classification model.

Technical deployment of aggregator business models

The proliferation of Distributed Energy Resources (DERs) is paving the way for new energy-efficient services that aim to make end-users more active. According to the literature, these services will be managed by a central figure, aggregators. This paper proposes several business models to accommodate them in the electric power industry. Several potential alternatives are identified from the study of different design elements, such as the control strategy, architecture, and signals that need to be exchanged. These alternatives are then tested by analyzing pairwise relationships between all the actors involved (Aggregator-Prosumers, Aggregator-DSO, and Aggregator-TSO). Every business model is first explained individually, including how contracts would operate, in order to determine their advantages and drawbacks. Finally, a comparison of all the alternatives is put forward together with an overview of the main initiatives that have already been implemented.

Mobility and energy efficient services composition algorithm with QoS guarantee for large scale Cyber–Physical–Social Systems

Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the existing approaches have certain limitations, particularly in situations where services may move from one location to another, become unavailable due to intensive battery usage, encounter failures, or undergo a decline in quality. These limitations often arise because these approaches do not simultaneously integrate mobility, energy, and QoS constraints while defining the user’s movement in a random manner. In this paper, the learning-based swarm optimization-aware service composition algorithm (LS-SCA) is proposed to overcome the aforementioned shortcoming. This approach surpasses existing ones by accounting simultaneously for the user’s mobility, energy, and QoS criteria during the service composition process. First, the Small World in Motion (SWIM) mobility model is employed in this study to determine the user’s mobility traces, avoiding the random generation of users’ traces. Second, an energy consumption model is proposed to increase the energy efficiency by avoiding the overuse of the devices’ batteries that can reduce the availability of services and lead to the composition failure. Third, the two-phase learning-based swarm optimizer (TPLSO) method is used in the composition process to find the sub-optimal composition that satisfies the global QoS constraints with the highest utility in terms of mobility, energy, and QoS. Unlike the most existing metaheuristic-based service composition approaches where the overall composition population is improved over a given number of iterations, the TPLSO method is exploited in this paper to improve only a subset of compositions, which reduces the composition time and increases the QoS utility of the composition. The simulation scenarios using two real datasets demonstrate that the LS-SCA approach outperforms six baselines in terms of energy consumption, QoS utility, and availability of composition. This notable performance makes the proposed approach more suitable for real-world applications where energy efficiency, QoS, and availability are crucial factors to consider.

Energy efficiency in 5G systems: A systematic literature review

To ensure Energy Efficiency (EE) and better Quality of service (QoS), it is necessary to analyze the energy saving possibilities for low resource utilization in the current networks caused by rigorous QoS requirements and implementing EE approach in the planned model for performance improvement. Distributed Denial of Service (DDoS) attacks aim to exhaust the network’s processing and communication capacity by saturating it with packets and generating malicious traffic. There are numerous advantages that make Digital Twin (DT) and Intrusion Detection technique (ID) an effective remedy for a range of (fifth generation) 5G problems. A DDoS attack must be immediately detected and stopped before a legitimate user can access the target of the attacker for the 5G network to provide an efficient energy service. Although they clearly show promise in assisting with the creation and implementation of the challenging 5G environment, Digital Twins is still a relatively new technology for 5G networks but will increase EE. In this research, a thorough examination of the materials was carried out to identify the most cutting-edge DT and ID methods. The purpose of this study was to comprehend the problems with Energy Efficiency, the need for DT, and the methods for dealing with large-scale attack by DDoS on Energy Efficient networks. Only 94 of the 1555 articles produced by the procedure were determined to be relevant using inclusion and exclusion criteria. The outcome demonstrates that in 5G networks, DT, and its fundamental approaches, like QoS and DDoS attack mitigation, can be used to regulate the network’s Energy Efficiency. Numerous practical applications focusing on 5G Systems use their own principles. The effectiveness of these strategies was evaluated using several assessment criteria, including DT, Intrusion Detection, QoS, Energy Efficiency, and 5G Systems. Each study issue is thoroughly explained, along with typical methods, advantages, disadvantages, and performance metrics. Energy economy, network reliability, privacy, and cost reduction are all considerably increased by the implementation of intrusion detection technology in 5G systems. The decision is supported by the technology’s demonstrated efficacy, scalability, real-time detection capacities, low error level, and personalized learning attributes, all of which contribute to the long-term viability of 5G networks as an entire system.

CFD Simulations of Small Particle Behavior with Blower-Driven Airflows in Single-Family Residential Buildings

Inhaling airborne droplets exhaled from an infected person is the principal mode of COVID-19 transmission. When residential energy efficiency workers conduct blower door tests in occupied residences with a COVID-19-infected occupant potentially present, there is a concern that it could put the workers at risk of infection with massive flows of air being generated by the tests. To minimize this risk, computational fluid dynamics (CFD) simulations were conducted for four prototype houses to develop guidelines for workers to follow during their service visits. The CFD simulations visualized the movements and evaluated the residence time of small particles released at certain locations under a series of scenarios representing situations that are likely to be encountered during in-home energy efficiency services. Guidelines were derived from the simulated tracks of droplets to help to increase the safety of the worker(s).

A multi-agent reinforcement learning-based method for server energy efficiency optimization combining DVFS and dynamic fan control

With the rapid development of the digital economy and intelligent industry, the energy consumption of data centers (DCs) has increased significantly. Various optimization methods are proposed to improve the energy efficiency of servers in DCs. However, existing solutions usually adopt model-based heuristics and best practices to select operations, which are not universally applicable. Moreover, existing works primarily focus on the optimization methods for individual components, with a lack of work on the joint optimization of multiple components. Therefore, we propose a multi-agent reinforcement learning-based method, named MRDF, combining DVFS and dynamic fan control to achieve a trade-off between power consumption and performance while satisfying thermal constraints. MRDF is model-free and learns by continuously interacting with the real server without prior knowledge. To enhance the stability of MRDF in dynamic environments, we design a data-driven baseline comparison method to evaluate the actual contribution of a single agent to the global reward. In addition, an improved Q-learning is proposed to deal with the large state and action space of the multi-core server. We implement MRDF on a Huawei Taishan 200 server and verify the effectiveness by running benchmarks. Experimental results show that the proposed method improves energy efficiency by an average of 3.9% compared to the best baseline solution, while flexibly adapting to different thermal constraints.

Enhancing the Wireless Network's Energy Efficiency to Reduce Security Challenges in 5G Systems: A Review

The desire for faster data speeds and increased Energy Efficiency has prompted the development of femtocells, which are short-range, low-cost, customer cellular access points. However, in a situation of Distributed Denial of Service (DDoS) which is caused by inefficient energy, distributed attack sources could be employed to amplify the assault and increase the attack's impact. By flooding the network with packets and creating malicious traffic, Distributed Denial of Service (DDoS) attacks try to deplete the network's communication and processing capability. A DDoS assault must be identified and neutralized quickly before a valid user can reach the attacker's target for 5G network to have an effective Energy Efficient service. For the next Fifth Generation (5G) Wireless Network, there is a pressing need to build an effective Energy Efficient mobile network solution. Despite their evident promise in assisting the development and deployment of the complicated 5G environment. The physical product, the digital product, and the relationship between both the physical and virtual goods are said to make up Digital Twin (DT). On the other hand, DT allows real-time communication with both the physical twins. The synergy of energy efficiency and security improvements in this research contributes to a more holistic optimization of 5G networks. This approach seeks to minimize energy consumption while fortifying the network against evolving security threats. Integrating energy-efficient practices with robust security measures enhances the overall resilience and sustainability of 5G systems. This is crucial for ensuring continuous, reliable, and secure communication in the face of dynamic challenges.

A hybrid control strategy for frequency regulation with variable refrigerant flow air conditioning system

Heating, ventilation and air conditioning (HVAC) systems of buildings are abundant sources for the Frequency Regulation (FR) ancillary service, whose actuating devices are controlled to follow the real-time power reference signals with the contractual capacity. For HVAC practice, model-based FR controls are hindered by expensive model acquisition. This paper presents a two-layer control strategy for HVAC systems to perform energy efficient FR service, with a rather low model requirement. At the lower level, the FR operation with a selected actuator is realized with a sliding mode control which is robust to power map variation. At the higher level, system efficiency is optimized in real time via a model-free constrained extremum seeking control (CESC). A conceptual variable refrigerant flow (VRF) system is used as the illustrative application, for which the outdoor unit fan offers the FR power reference tracking with SMC. At the outer loop, the compressor discharge pressure is adjusted by a CESC to minimize the total power consumption while the zonal temperature regulation is reinforced as equality constraint. The proposed strategy is evaluated with simulations on a Modelica simulation, and the results validate its effectiveness. The Net Energy Cost can reduce by 15% to 23% under different load levels.

Extreme contributions of conventional investments vis-à-vis Islamic ones to renewables

The growing global awareness of social and environmental issues has led investors to prioritize sustainable investments. This study examines the relationship between ten Islamic and conventional sector indices, which include stocks screened for environmental, social, and governance (ESG) factors, across three energy-related fields: renewable energy, energy efficiency, and oil equipment and services. Using a quantile cross-spectral approach, this research finds a high level of long-term synchronization among all energy-related sectors and indices. However, as markets shift from bearish to bullish, the current relationship between energy-related sectors and Islamic and conventional indices weakens. To support the findings in this research and investigate potential financial implications, this investigation applies a causality-in-quantiles approach. The research in this study shows a bidirectional causal relationship between conventional and Islamic indices, particularly in the energy efficiency sector during bearish market conditions. At lower tails, ESG market indices are found to cause renewable energy products, suggesting a hedging mechanism. The findings of this study assert that investments that do not possess any ESG shortcomings are imperative for effectively bolstering renewable energy companies and fostering opportunities for energy efficiency. These conclusions hold considerable ramifications for energy policymakers as they strive to devise more robust financial mechanisms that can provide substantial support to the burgeoning green sector.

Designing Smart Antennas Using Machine Learning Algorithms

Smart antenna technologies improve spectral efficiency, security, energy efficiency, and overall service quality in cellular networks by utilizing signal processing algorithms that provide radiation beams to users while producing nulls for interferers. In this paper, the performance of such ML solutions as the support vector machine (SVM) algorithm, the artificial neural network (ANN), the ensemble algorithm (EA), and the decision tree (DT) algorithm used for forming the beam of smart antennas are compared. A smart antenna array made up of 10 half-wave dipoles is considered. The ANN method is better than the remaining approaches when it comes to achieving beam and null directions, whereas EA offers better performance in terms of reducing the side lobe level (SLL). The maximum SLL is achieved using EA for all the user directions. The performance of the ANN algorithm in terms of forming the beam of a smart antenna is also compared with that of the variable-step size adaptive algorithm.

Joint optimization of energy conservation and transfer passenger service quality in rail transit system

ABSTRACT The complex electricity transmission process and transfer passenger demand in the rail transit system make it challenging to formulate optimal timetables and train speed profiles to meet the needs of the system. This study optimizes train timetable to meet energy conservation and passenger service quality of cross-mode rail transit system during different operation periods. Firstly, based on the spatiotemporal travel data of transfer passengers, the train-time matrix is proposed to construct a bi-objective optimization model (BOOM) that incorporates the total energy consumption optimization model (TECOM) and total waiting time optimization model of transfer passenger (TWTOM). It can better combine the passenger transport scheme and the train energy-saving operation strategy, thus improving energy-saving performance and passenger transport efficiency and simplifying the difficulty of mathematical modelling of such BOOM. Then, a method for determining the appropriate weight coefficient ratio and the improved non-dominated sorting genetic algorithm II (NSGA-II) are applied to obtain the efficient Pareto front solutions. Taking the numerical experiments as test cases, the improved NSGA-II can get a efficient solution faster than the traditional NSGA-II. Finally, two numerical experiments using real-world data are conducted to verify the practicability of the optimization model and the effectiveness of improved NSGA-II. The optimization results can reduce energy consumption by up to 11.02% and passenger waiting time by 137,320 s, respectively. This study helps to improve the energy efficiency and passenger service quality of cross-mode rail transit systems, and also provides a reference for dispatchers to optimize train timetable.

A Review of Hybrid VLC/RF Networks: Features, Applications, and Future Directions.

The expectation for communication systems beyond 5G/6G is to provide high reliability, high throughput, low latency, and high energy efficiency services. The integration between systems based on radio frequency (RF) and visible light communication (VLC) promises the design of hybrid systems capable of addressing and largely satisfying these requirements. Hybrid network design enables complementary cooperation without interference between the two technologies, thereby increasing the overall system data rate, improving load balancing, and reducing non-coverage areas. VLC/RF hybrid networks can offer reliable and efficient communication solutions for Internet of Things (IoT) applications such as smart lighting, location-based services, home automation, smart healthcare, and industrial IoT. Therefore, hybrid VLC/RF networks are key technologies for next-generation communication systems. In this paper, a comprehensive state-of-the-art study of hybrid VLC/RF networks is carried out, divided into four areas. First, indoor scenarios are studied considering lighting requirements, hybrid channel models, load balancing, resource allocation, and hybrid network topologies. Second, the characteristics and implementation of these hybrid networks in outdoor scenarios with adverse conditions are analyzed. Third, we address the main applications of hybrid VLC/RF networks in technological, economic, and socio-environmental domains. Finally, we outline the main challenges and future research lines of hybrid VLC/RF networks.

A Light Weight Optimal Resource Scheduling Algorithm for Energy Efficient and Real Time Cloud Services

Abstract: Cloud computing has become an essential technology for providing various services to customers. Energy consumption and response time are critical factors in cloud computing systems. Therefore, scheduling algorithms play a vital role in optimizing resource usage and improving energy efficiency. In this paper, we propose a lightweight optimal scheduling algorithm for energy-efficient and real-time cloud services. Our algorithm considers the trade-off between energy consumption and response time and provides a solution that optimizes both factors. We evaluate our algorithm on a cloud computing testbed using the Cloud Sim simulation toolkit and compare it with load balancing round robin, ant colony, genetic algorithm, and analytical algorithm. The results show that our proposed algorithm outperforms these algorithms in terms of energy efficiency and response time.

Nuclear-Driven Integrated Energy Systems: A State-of-the-Art Review

Because of the growing concerns regarding climate change and energy sustainability, a transition toward a modern energy sector that reduces environmental effects while promoting social and economic growth has gained traction in recent years. Sustainable energy solutions, which include renewable and low-carbon sources such as nuclear energy and natural gas, could minimize emissions of greenhouse gases, enhance air and water quality, and encourage energy independence. Yet, the shift to a sustainable energy industry is fraught with difficulties, including governmental and regulatory obstacles, technological and economic limits, and societal acceptability hurdles. Addressing these issues would necessitate the development of long-term, durable, and cost-effective energy systems containing nuclear energy and associated with the generation of both electricity and other by-products required by industry. Integrated energy systems (IES) are a novel way to maximize the use of various energy resources and technologies in order to deliver dependable, efficient, and sustainable energy services. IES entail the integration of various energy systems, such as electricity, heating, cooling, and transportation, in respect to energy sustainability and a system’s resilience and flexibility. Their development and implementation require the cooperation of several parties, including energy providers and policymakers. This study provides a state-of-the-art literature review of the most creative nuclear-driven hybrid energy system applications and methodologies, from which the research challenges and prospects for effective IES implementation emerge.

KPI ON THE WAY OF FORMING AN ENERGY-EFFICIENT SOCIETY. 25 YEARS OF EXPERIENCE

Given the energy dependence of our state, the rational use of resource potential is a strategic direction for the development of the Ukrainian economy. Increasing energy efficiency is an important condition for the successful implementation of climate policy. The modern market of energy efficient services requires a significant number of trained specialists in the field of energy audit, energy management, energy consumption management, energy efficiency certification, heat and electrical engineering, process automation in industry and housing and communal services. In 2021, the Educational and Scientific Institute of Energy Saving and Energy Management (NN IEE) Igor Sikorsky Kyiv Polytechnic Institute Igor Sikorsky, since 1997 heir to the Faculty of Mining (GF), the Faculty of Mining Electromechanics and Automation (FGEMA), the Faculty of Mining Technology (GTF), celebrated 75 years since its founding. And already in the current 2022, we have another significant and pleasant event – 25 years of the creation of the specialty «Energy Management». By a joint order of the Ministry of Education and the State Committee for Energy Saving of Ukraine (No. 137/45 dated May 07, 1997), IEE was defined as the basis for training specialists in this specialty. The article reflects the main stages of the formation and development of the institute, scientific achievements, the contribution of the team to the training and advanced training of personnel and educational activities in the field of energy efficiency. NN IEE, as the founder of the «Energy Management» specialty, has gone through a long path of formation and development. ІЕЕ prepares students according to the educational programs «Energy Management and Energy-Efficient Technologies», «Systems of Supplying Consumers with Electric Energy».

Innovative Data-Driven Energy Services and Business Models in the Domestic Building Sector

The market of energy services for the residential sector in Europe is very limited at present. Various reasons can be argued such as the high transaction costs in a highly fragmented market and the low energy consumption per dwelling. The rather long payback time for investments render Energy Services Companies’ (ESCOs) services financially unattractive for many ESCOs and building residents, thus hindering a large potential of energy savings in a sector that is responsible of almost half of Europe’s energy consumption. If the ambitious 2030 and 2050’s decarbonisation targets are to be met, the EU’s residential sector must be part of the solution. This paper offers insights about novel ESCO business models based on intensive data-driven Artificial Intelligence algorithms and analytics that enable the deployment of smart energy services in the domestic sector under a Pay-for-Performance (P4P) approach. The combination of different sources of energy efficiency services and the optimal participation of domestic consumers in aggregated demand response (DR) schemes open the door to new revenue streams for energy service providers and building residents and reduce the hitherto long payback periods of ESCOs services in the sector. Innovative business models for ESCOs and demand flexibility Aggregators are thoroughly described. Especially customised Performance Measurement and Verification protocols enable fair and transparent P4P ESCO contracts. The new human-centric energy and non-energy services increase the energy consumption awareness of building users and deploy behavioural and automated responses to both environmental and market signals to maximise the economic benefit for both energy service providers and consumers, always respecting data protection rules and the consumers’ comfort preferences. The new hybrid business models of P4P energy services make traditional EPC more attractive to energy service providers, with low cost data collection and treatment systems to bring payback periods below 10 years in the residential building sector.

Energy-Efficient Service Migration for Multi-User Heterogeneous Dense Cellular Networks

Mobile edge computing (MEC) is a key enabler for ultra-low latency in heterogeneous dense cellular networks in the 5G era and beyond, by deploying services at the network edge. Due to high user mobility, the services are usually migrated to follow the users by predicting the user trajectory to achieve a balance between energy consumption and service latency. However, service migration for multi-user heterogeneous dense cellular networks is challenging because (1) the user trajectory prediction, which is crucial for service migration, becomes intractable with a large number of users, and (2) making service migration decisions for each user independently is subjected to interference among the users. Therefore, in this study, we formulated the service migration of all the users in MEC-enabled heterogeneous dense cellular networks as an optimization problem, with the objective of minimizing the average energy consumption while satisfying the service latency requirements, taking into account the interference among different users. Next, we developed an efficient energy-efficient online algorithm based on the Lyapunov and particle swarm optimizations, called EGO, to resolve the original problem without predicting the trajectories of the users. Finally, a series of simulations based on real-world mobility traces of vehicles in Bologna were conducted to establish the superiority of the EGO algorithm over state-of-the-art solutions.

A novel dynamic load-priority-based scheduling strategy for home energy management system

In order to increase renewable energy consumption and enhance energy efficiency, residents participate in the utility in the form of demand response. To address the challenges of imprecise energy efficiency services and inadequate response of household energy use, a novel dynamic-load-priority-based scheduling strategy is proposed. Firstly, according to the operation law of household appliances, the control models of three kinds of flexible loads are established respectively. Secondly, according to the characteristics of household appliances, the priority evaluation index of household appliances including user comfort, electricity cost and task urgency index is proposed. The Technique for Order Preference by Similarity to Ideal Solution based on entropy weight is used to prioritize the household appliances that control the scheduling queue. Finally, according to the sorting results, the load control strategy is implemented to reasonably arrange the orderly work of household electrical equipment. Compared with the uncontrolled state, the proposed strategy can limit the total power of appliance load operation to the demand range in the peak load condition, and save 3% of electricity cost and reduce 5.4 kW of peak load under the demand limit of 12 kW, and 32% of electricity cost and 10.6 kW of peak load under the demand limit of 7 kW, without affecting the basic life of customers. In the period of new energy saturation, the load can also be dispatched to participate in the new energy consumption, thus realizing the coordinated interaction between residential energy consumption and the utility.

Organic management of cattle and pigs in Mediterranean systems: energy efficiency and ecosystem services

Organic management of cattle and pigs in Mediterranean systems: energy efficiency and ecosystem services